Knowledge Discovery from Functional Brain Images by Logical Regression Analysis

Abstract

SummaryAs a result of the ongoing development of non-invasive analysis of brain function, detailed brain images can be obtained, from which the relations between brain areas and brain functions can be understood. The relations between brain areas and brain functions are described by rules. Knowledge discovery from functional brain images is knowledge discovery from pattern data, which is a new field different from knowledge discovery from symbolic data or numerical data. We have been developing a new method called Logical Regression Analysis. The Logical Regression Analysis consists of two steps. The first step is a regression analysis. The second step is rule extraction from the regression formula obtained by the regression analysis. In this paper, we apply the Logical Regression Analysis to functional brain images to discover relations between a brain function and brain areas. We use nonparametric regression analysis as a regression analysis, since there are not sufficient data to obtain linear formulas using conventional linear regression from functional brain images. Experimental results show that the algorithm works well for real data.